{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./log.txt',header=None,sep='\\t')# 根据制表符进行分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','createdate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
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       "      <th>interval</th>\n",
       "      <th>createdate</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
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       "      <td>88.75</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
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       "      <td>749.12</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           createdate  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78185</th>\n",
       "      <td>6047062</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>582.23</td>\n",
       "      <td>67.92</td>\n",
       "      <td>227.77</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-31 11:33:50</td>\n",
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       "    <tr>\n",
       "      <th>100309</th>\n",
       "      <td>7448809</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1173.28</td>\n",
       "      <td>85.95</td>\n",
       "      <td>315.44</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 12:11:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177054</th>\n",
       "      <td>13252659</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>335.92</td>\n",
       "      <td>145.07</td>\n",
       "      <td>190.85</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-28 11:58:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98399</th>\n",
       "      <td>7312744</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1359.42</td>\n",
       "      <td>94.55</td>\n",
       "      <td>253.45</td>\n",
       "      <td>151.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-26 21:36:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135161</th>\n",
       "      <td>10022214</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>1308.23</td>\n",
       "      <td>143.40</td>\n",
       "      <td>382.02</td>\n",
       "      <td>261.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-10 18:22:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "78185    6047062  /front-api/bill/create      4        582.23         67.92   \n",
       "100309   7448809  /front-api/bill/create      7       1173.28         85.95   \n",
       "177054  13252659  /front-api/bill/create      2        335.92        145.07   \n",
       "98399    7312744  /front-api/bill/create      9       1359.42         94.55   \n",
       "135161  10022214  /front-api/bill/create      5       1308.23        143.40   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           createdate  \n",
       "78185         227.77         145.0        60  2019-01-31 11:33:50  \n",
       "100309        315.44         167.0        60  2019-03-01 12:11:42  \n",
       "177054        190.85         167.0        60  2019-05-28 11:58:19  \n",
       "98399         253.45         151.0        60  2019-02-26 21:36:38  \n",
       "135161        382.02         261.0        60  2019-04-10 18:22:26  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) # 随机采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('api',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2018-11-10 22:24:26\n",
       "freq                        1\n",
       "Name: createdate, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['createdate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   createdate    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th>52412</th>\n",
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       "      <th>52416</th>\n",
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       "      <td>3</td>\n",
       "      <td>593.41</td>\n",
       "      <td>82.03</td>\n",
       "      <td>312.03</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53287</th>\n",
       "      <td>4397160</td>\n",
       "      <td>4</td>\n",
       "      <td>584.47</td>\n",
       "      <td>89.48</td>\n",
       "      <td>242.96</td>\n",
       "      <td>146.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53288</th>\n",
       "      <td>4397232</td>\n",
       "      <td>1</td>\n",
       "      <td>90.61</td>\n",
       "      <td>90.61</td>\n",
       "      <td>90.61</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:56:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53289</th>\n",
       "      <td>4397304</td>\n",
       "      <td>3</td>\n",
       "      <td>502.90</td>\n",
       "      <td>143.42</td>\n",
       "      <td>187.13</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:57:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53290</th>\n",
       "      <td>4397368</td>\n",
       "      <td>2</td>\n",
       "      <td>459.39</td>\n",
       "      <td>228.96</td>\n",
       "      <td>230.43</td>\n",
       "      <td>229.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:58:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53291</th>\n",
       "      <td>4397449</td>\n",
       "      <td>5</td>\n",
       "      <td>710.47</td>\n",
       "      <td>110.06</td>\n",
       "      <td>175.36</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:59:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>880 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "52412  4334654      5        838.89        106.85        289.56         167.0   \n",
       "52413  4334717      7        932.25         79.51        168.52         133.0   \n",
       "52414  4334800      2        180.24         81.97         98.27          90.0   \n",
       "52415  4334813      5        856.20         89.40        209.12         171.0   \n",
       "52416  4334919      3        593.41         82.03        312.03         197.0   \n",
       "...        ...    ...           ...           ...           ...           ...   \n",
       "53287  4397160      4        584.47         89.48        242.96         146.0   \n",
       "53288  4397232      1         90.61         90.61         90.61          90.0   \n",
       "53289  4397304      3        502.90        143.42        187.13         167.0   \n",
       "53290  4397368      2        459.39        228.96        230.43         229.0   \n",
       "53291  4397449      5        710.47        110.06        175.36         142.0   \n",
       "\n",
       "       interval           createdate  \n",
       "52412        60  2019-01-01 00:00:56  \n",
       "52413        60  2019-01-01 00:01:56  \n",
       "52414        60  2019-01-01 00:02:56  \n",
       "52415        60  2019-01-01 00:03:56  \n",
       "52416        60  2019-01-01 00:04:56  \n",
       "...         ...                  ...  \n",
       "53287        60  2019-01-01 23:55:57  \n",
       "53288        60  2019-01-01 23:56:57  \n",
       "53289        60  2019-01-01 23:57:57  \n",
       "53290        60  2019-01-01 23:58:57  \n",
       "53291        60  2019-01-01 23:59:57  \n",
       "\n",
       "[880 rows x 8 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.createdate >='2019-01-01' ) & (df.createdate <='2019-01-02' )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index # 查看当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['createdate'] # 替换索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>createdate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "createdate                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval           createdate  \n",
       "createdate                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.createdate) # 将object的索引转化为时间类型的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='createdate', length=179496, freq=None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>createdate</th>\n",
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       "    <tr>\n",
       "      <th>createdate</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:56</th>\n",
       "      <td>4334654</td>\n",
       "      <td>5</td>\n",
       "      <td>838.89</td>\n",
       "      <td>106.85</td>\n",
       "      <td>289.56</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 00:00:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:01:56</th>\n",
       "      <td>4334717</td>\n",
       "      <td>7</td>\n",
       "      <td>932.25</td>\n",
       "      <td>79.51</td>\n",
       "      <td>168.52</td>\n",
       "      <td>133.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 00:01:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:02:56</th>\n",
       "      <td>4334800</td>\n",
       "      <td>2</td>\n",
       "      <td>180.24</td>\n",
       "      <td>81.97</td>\n",
       "      <td>98.27</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 00:02:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:03:56</th>\n",
       "      <td>4334813</td>\n",
       "      <td>5</td>\n",
       "      <td>856.20</td>\n",
       "      <td>89.40</td>\n",
       "      <td>209.12</td>\n",
       "      <td>171.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 00:03:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:04:56</th>\n",
       "      <td>4334919</td>\n",
       "      <td>3</td>\n",
       "      <td>593.41</td>\n",
       "      <td>82.03</td>\n",
       "      <td>312.03</td>\n",
       "      <td>197.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 00:04:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 23:55:57</th>\n",
       "      <td>4397160</td>\n",
       "      <td>4</td>\n",
       "      <td>584.47</td>\n",
       "      <td>89.48</td>\n",
       "      <td>242.96</td>\n",
       "      <td>146.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 23:56:57</th>\n",
       "      <td>4397232</td>\n",
       "      <td>1</td>\n",
       "      <td>90.61</td>\n",
       "      <td>90.61</td>\n",
       "      <td>90.61</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:56:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 23:57:57</th>\n",
       "      <td>4397304</td>\n",
       "      <td>3</td>\n",
       "      <td>502.90</td>\n",
       "      <td>143.42</td>\n",
       "      <td>187.13</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:57:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 23:58:57</th>\n",
       "      <td>4397368</td>\n",
       "      <td>2</td>\n",
       "      <td>459.39</td>\n",
       "      <td>228.96</td>\n",
       "      <td>230.43</td>\n",
       "      <td>229.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:58:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 23:59:57</th>\n",
       "      <td>4397449</td>\n",
       "      <td>5</td>\n",
       "      <td>710.47</td>\n",
       "      <td>110.06</td>\n",
       "      <td>175.36</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-01 23:59:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>880 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdate                                                                      \n",
       "2019-01-01 00:00:56  4334654      5        838.89        106.85        289.56   \n",
       "2019-01-01 00:01:56  4334717      7        932.25         79.51        168.52   \n",
       "2019-01-01 00:02:56  4334800      2        180.24         81.97         98.27   \n",
       "2019-01-01 00:03:56  4334813      5        856.20         89.40        209.12   \n",
       "2019-01-01 00:04:56  4334919      3        593.41         82.03        312.03   \n",
       "...                      ...    ...           ...           ...           ...   \n",
       "2019-01-01 23:55:57  4397160      4        584.47         89.48        242.96   \n",
       "2019-01-01 23:56:57  4397232      1         90.61         90.61         90.61   \n",
       "2019-01-01 23:57:57  4397304      3        502.90        143.42        187.13   \n",
       "2019-01-01 23:58:57  4397368      2        459.39        228.96        230.43   \n",
       "2019-01-01 23:59:57  4397449      5        710.47        110.06        175.36   \n",
       "\n",
       "                     res_time_avg  interval           createdate  \n",
       "createdate                                                        \n",
       "2019-01-01 00:00:56         167.0        60  2019-01-01 00:00:56  \n",
       "2019-01-01 00:01:56         133.0        60  2019-01-01 00:01:56  \n",
       "2019-01-01 00:02:56          90.0        60  2019-01-01 00:02:56  \n",
       "2019-01-01 00:03:56         171.0        60  2019-01-01 00:03:56  \n",
       "2019-01-01 00:04:56         197.0        60  2019-01-01 00:04:56  \n",
       "...                           ...       ...                  ...  \n",
       "2019-01-01 23:55:57         146.0        60  2019-01-01 23:55:57  \n",
       "2019-01-01 23:56:57          90.0        60  2019-01-01 23:56:57  \n",
       "2019-01-01 23:57:57         167.0        60  2019-01-01 23:57:57  \n",
       "2019-01-01 23:58:57         229.0        60  2019-01-01 23:58:57  \n",
       "2019-01-01 23:59:57         142.0        60  2019-01-01 23:59:57  \n",
       "\n",
       "[880 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['id','interval'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdate                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           createdate  \n",
       "createdate                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   count         179496 non-null  int64  \n",
      " 1   res_time_sum  179496 non-null  float64\n",
      " 2   res_time_min  179496 non-null  float64\n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_avg  179496 non-null  float64\n",
      " 5   createdate    179496 non-null  object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins=30,width=0.5) # 初步分析count,直方图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576a609b50>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 切出一天的数据，绘制一天时段的接口调用情况\n",
    "df['2019-5-1']['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_day = df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df_day[['count']].resample('1H').mean() # 重新按小时采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "createdate                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576b4a5220>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 柱状图\n",
    "plt.figure(figsize=(10,3)) # 单位为英寸\n",
    "df2['count'].plot(kind='bar')\n",
    "plt.xticks(rotation = 60) # 文字倾斜\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LwX+2iS5mgL+NiDMppXLn/wKpOV5GKEmZcgpFkjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlKn/A6+SRiLXU0nxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析有没有异常值,访问接口异常频繁\n",
    "# 箱线图\n",
    "df['2019-05-01'][['count']].boxplot(showmeans = True,meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdate                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           createdate  \n",
       "createdate                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576e265190>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 某一天的响应时间\n",
    "df['2019-05-01']['res_time_avg'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576b4d06a0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-05-01'][['res_time_avg']].boxplot(showmeans = True,meanline = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdate                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           createdate  \n",
       "createdate                                              \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-05-01']\n",
    "df2[df2['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576ded2fd0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2019-05-01 00:34:48\t1\t1694.47\t1694.47\t1694.47\t1694.0\t2019-05-01 00:34:48定义为异常值\n",
    "df['2019-05-01'].resample('20T').mean()[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576e09d910>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 业务高峰时段为下午2-3，晚上7,8，响应时间上升\n",
    "df['2019-05-01':'2019-5-10']['count'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
       "           dtype='int64', name='createdate', length=865)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-05-02'].index.weekday #0代表周一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdate</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdate                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "\n",
       "                     res_time_avg           createdate  weekday  \n",
       "createdate                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdate</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdate                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "\n",
       "                     res_time_avg           createdate  weekday  weekend  \n",
       "createdate                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断是否是周末\n",
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对周末进行分组,求均值\n",
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  createdate\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576e0ca3a0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 周末和非周末，具体时间对比。\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "createdate                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重叠周末与非周末\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2576e887b80>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
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}
